The centralized training of deep learning models not only incurs high communication cost of data transfer into the cloud systems but it also raises the privacy-protection concerns of data providers. Federated learning is the new approach whereby centralized deep learning models are distributed across the devices and systems near the data sources which perform initial training and report the updated model attributes to the centralized cloud servers. These centralized servers perform secure and privacy preserving attributes-aggregation and update the global learning model. Federated learning ensures communication efficiency because the raw data never leaves the premises of data providers. In addition, secure federated aggregation benefits in terms of privacy-preservation where neither the centralized servers nor other data providers in the federated learning systems can discriminate the actual data. Still, federated learning systems face key challenges that may hinder their massive adoption. These challenges include privacy, latency, decentralization, asynchronization, personalization, fairness, and bandwidth-optimization, to name a few. This book aims at congregating researchers and practitioners to share their research in showing how federated learning can transform next-generation artificial intelligence applications, and propose solutions to address key federated learning challenges. We are interested in both survey and original works in unexplored and/or emerging topics in the broad area of federated learning systems, architectures, applications, and algorithms, and in novel findings and/or new insights that build on existing works. Our topics of interest include but not limited to:

Abstract Submission Due:May 30, 2020

Abstract Notification Due: June 15, 2020

Full Chapter Submission Due :August 31, 2020

Full Chapter Acceptance Notification: September 30, 2020

Camera-Ready Chapter Submission: October 31, 2020

Book Publication: December 31, 2020

Submission Guidelines

- Authors should only submit original work that has neither appeared elsewhere for publication, nor is presently under review for another refereed publication. Extensions of previously published works are welcome as long as the contributions made in the extended version are significant to warrant publication.

- Authors are expected to submit their chapter proposals which should include abstract and proposed outline by the specified deadline of May 30, 2020 at the following addresses - Muhammad Habib ur Rehman (mhrehman@ieee.org) - Mohamed Medhat Gaber (mohamed.m.gaber@gmail.com)

- Each full chapter submission will be independently reviewed by at least three members of the Expert Technical Program Committee (ETPC). They will be evaluated according to the novelty, originality and applicability of the proposed manuscript. The authors of each submission, successful or otherwise, will be notified by September30, 2020

- What to submit?You must submit a ZIP file via EasyChair containing the items described in the section "manuscript submission checklist" (Instructions for Authors) by the specified deadline of August31, 2020

- Important Note: Authors are expected to submit the latex files only (i.e. ,tex, .bib, and/or supporting PDF and Graphics).